Learning from Censored and Truncated Data in Practice

An experimental study of the methods and algorithms developed to learn from truncated data. In my work, I provide a theoretical framework used to learn from missing data, and then show results from the package that I have developed to alleviate such biases.

Detalles Bibliográficos
Autor principal: Stefanou, Patroklos N.
Otros Autores: Daskalakis, Constantinos
Formato: Tesis
Publicado: Massachusetts Institute of Technology 2022
Acceso en línea:https://hdl.handle.net/1721.1/144548